

'''simple docstring'''





from typing import TYPE_CHECKING

from ...utils import (
    OptionalDependencyNotAvailable,
    _LazyModule,
    is_tf_available,
    is_torch_available,
    is_vision_available,
)


A_	= {
    "configuration_efficientformer": [
        "EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP",
        "EfficientFormerConfig",
    ]
}

try:
				if not is_vision_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_	= ["EfficientFormerImageProcessor"]

try:
				if not is_torch_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_	= [
				    "EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "EfficientFormerForImageClassification",
				    "EfficientFormerForImageClassificationWithTeacher",
				    "EfficientFormerModel",
				    "EfficientFormerPreTrainedModel",
				]

try:
				if not is_tf_available():
								raise OptionalDependencyNotAvailable()
except OptionalDependencyNotAvailable:
				pass
else:
				A_	= [
				    "TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST",
				    "TFEfficientFormerForImageClassification",
				    "TFEfficientFormerForImageClassificationWithTeacher",
				    "TFEfficientFormerModel",
				    "TFEfficientFormerPreTrainedModel",
				]

if TYPE_CHECKING:
				from .configuration_efficientformer import EFFICIENTFORMER_PRETRAINED_CONFIG_ARCHIVE_MAP, EfficientFormerConfig

				try:
								if not is_vision_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .image_processing_efficientformer import EfficientFormerImageProcessor

				try:
								if not is_torch_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_efficientformer import (
								    EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    EfficientFormerForImageClassification,
								    EfficientFormerForImageClassificationWithTeacher,
								    EfficientFormerModel,
								    EfficientFormerPreTrainedModel,
								)
				try:
								if not is_tf_available():
												raise OptionalDependencyNotAvailable()
				except OptionalDependencyNotAvailable:
								pass
				else:
								from .modeling_tf_efficientformer import (
								    TF_EFFICIENTFORMER_PRETRAINED_MODEL_ARCHIVE_LIST,
								    TFEfficientFormerForImageClassification,
								    TFEfficientFormerForImageClassificationWithTeacher,
								    TFEfficientFormerModel,
								    TFEfficientFormerPreTrainedModel,
								)

else:
				import sys

				A_	= _LazyModule(__name__, globals()["__file__"], _import_structure, module_spec=__spec__)




from transformers import BertTokenizer, EncoderDecoderModel, SeqaSeqTrainer, SeqaSeqTrainingArguments
from transformers.testing_utils import TestCasePlus, require_torch, slow
from transformers.utils import is_datasets_available


if is_datasets_available():
		import datasets




class   _UpperCamelCase (		_UpperCAmelCase					):


			"""simple docstring"""






			@slow
			@require_torch
			def 			_SCREAMING_SNAKE_CASE     (  self		)	->   Any:

							'''simple docstring'''


							__lowercase					     =				EncoderDecoderModel.from_encoder_decoder_pretrained('''prajjwal1/bert-tiny''' ,  '''prajjwal1/bert-tiny'''		)
							__lowercase					     =				BertTokenizer.from_pretrained('''bert-base-uncased'''		)

							__lowercase					     =				bertabert.config.encoder.vocab_size
							__lowercase					     =				tokenizer.sep_token_id
							__lowercase					     =				tokenizer.cls_token_id
							__lowercase					     =				1_28

							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''train[:1%]'''		)
							__lowercase					     =				datasets.load_dataset('''cnn_dailymail''' ,  '''3.0.0''' ,  split='''validation[:1%]'''		)

							__lowercase					     =				train_dataset.select(range(32		)		)
							__lowercase					     =				val_dataset.select(range(16		)		)

							__lowercase					     =				4

							def _map_to_encoder_decoder_inputs(lowerCAmelCase__		):
											# Tokenizer will automatically set [BOS] <text> [EOS]
											__lowercase					     =				tokenizer(batch['''article'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=5_12		)
											__lowercase					     =				tokenizer(batch['''highlights'''] ,  padding='''max_length''' ,  truncation=lowerCAmelCase__ ,  max_length=1_28		)
											__lowercase					     =				inputs.input_ids
											__lowercase					     =				inputs.attention_mask

											__lowercase					     =				outputs.input_ids
											__lowercase					     =				outputs.input_ids.copy()
											__lowercase					     =				[
											    [-1_00 if token == tokenizer.pad_token_id else token for token in labels] for labels in batch['''labels''']
											]
											__lowercase					     =				outputs.attention_mask

											assert all(len(lowerCAmelCase__		) == 5_12 for x in inputs.input_ids		)
											assert all(len(lowerCAmelCase__		) == 1_28 for x in outputs.input_ids		)

											return batch

							def _compute_metrics(lowerCAmelCase__		):
											__lowercase					     =				pred.label_ids
											__lowercase					     =				pred.predictions

											# all unnecessary tokens are removed
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)
											__lowercase					     =				tokenizer.batch_decode(lowerCAmelCase__ ,  skip_special_tokens=lowerCAmelCase__		)

											__lowercase					     =				sum([int(pred_str[i] == label_str[i]		) for i in range(len(lowerCAmelCase__		)		)]		) / len(lowerCAmelCase__		)

											return {"accuracy": accuracy}

							# map train dataset
							__lowercase					     =				train_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							train_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							# same for validation dataset
							__lowercase					     =				val_dataset.map(
							    _map_to_encoder_decoder_inputs ,  batched=lowerCAmelCase__ ,  batch_size=lowerCAmelCase__ ,  remove_columns=['''article''', '''highlights'''] ,  )
							val_dataset.set_format(
							    type='''torch''' ,  columns=['''input_ids''', '''attention_mask''', '''decoder_input_ids''', '''decoder_attention_mask''', '''labels'''] ,  )

							__lowercase					     =				self.get_auto_remove_tmp_dir()

							__lowercase					     =				SeqaSeqTrainingArguments(
							    output_dir=lowerCAmelCase__ ,  per_device_train_batch_size=lowerCAmelCase__ ,  per_device_eval_batch_size=lowerCAmelCase__ ,  predict_with_generate=lowerCAmelCase__ ,  evaluation_strategy='''steps''' ,  do_train=lowerCAmelCase__ ,  do_eval=lowerCAmelCase__ ,  warmup_steps=0 ,  eval_steps=2 ,  logging_steps=2 ,  )

							# instantiate trainer
							__lowercase					     =				SeqaSeqTrainer(
							    model=lowerCAmelCase__ ,  args=lowerCAmelCase__ ,  compute_metrics=_compute_metrics ,  train_dataset=lowerCAmelCase__ ,  eval_dataset=lowerCAmelCase__ ,  tokenizer=lowerCAmelCase__ ,  )

							# start training
							trainer.train()